Many recent advances in wireless transmission have rested on the use of multiple antennas for transmission and reception. Multiple antennas, fundamentally, can provide an increase in the numbers of Degrees of Freedom (DoFs) that can be exploited by a wireless system for transmission, i.e., the number of scalar data streams that can be simultaneously transmitted to the receiving parties in the system. Here, DoFs can be used to provide increased spectral efficiency (throughput) and/or added diversity (robustness). Indeed, a Single User MIMO (SU-MIMO) system with NT transmission antennas serving a single user with NR receive antennas may be able to exploit up to min(NT, NR) DoFs for downlink transmission. These DoFs, can under certain conditions be used to improve throughput by a factor that grows linearly with min(NT, NR). Such benefits of MIMO, and increased DoFs, are behind much of the interest in using MIMO in new and future systems.
Exploiting such DoFs often requires some amount of cost to the system. One such cost is knowledge of the channel state between transmitting and receiving antennas. Such Channel State Information (CSI) often has to be available to either the transmitter (such CSI is termed CSIT) and/or to the receiver (such CSI is termed CSIR). The DoFs available also depend on having sufficient “richness” in the channels between transmitting and receiving antennas.
For example, SU-MIMO CSIR-based systems such as Bit Interleaved Coded Modulation (BICM) and D-BLAST can achieve the maximum possible DoFs of min(NT, NR) under suitable channel conditions. Under such conditions, they therefore can be used to provide corresponding linear increases in spectral efficiency. Such designs are well understood by those familiar with the state of the art.
Similarly, a Multi-User MIMO (MU-MIMO) system with NT transmission antennas at the base station (BS) and K single-antenna users (NR=1) can provide up to min(NT, K) DoFs. As in the case of SU-MIMO, MU-MIMO can be used to improve throughput linearly with min(NT, K).
However, unlike SU-MIMO, many MU-MIMO techniques (in fact most if not all of the prevailing MU-MIMO techniques used and studied for standards) require knowledge of CSIT. MU-MIMO based on CSIT, unlike SU-MIMO based on CSIR, requires additional overheads to estimate CSI and feedback CSI to transmitters before the transmission can even take place (see Caire et al., “Multiuser MIMO achievable rates with downlink training and channel state,” in IEEE Transactions on Information Theory, June 2010, pp. 2845-2866).
Despite such overheads, MU-MIMO is of practical interest since it has the benefit over SU-MIMO of being able to grow the DoFs without having to add many receive antennas, radio frequency (RF) chains, or increase processing (e.g., decoding) complexity to portable or mobile devices.
The issue of CSI overhead is a fundamental issue that should not be overlooked in assessing such conventional MIMO. Such CSI-related overhead in fact can represent a fundamental “dimensionality bottleneck” that can limit the net spectral efficiency increase that can be obtained with conventional CSI-dependent MIMO.
In particular, if one wants to continue to exploit the growth in DoFs (e.g., linear growth) by increasing NT (or NR or K), one also has to consider how to support increased system overhead in obtaining the CSI required to formulate transmissions and decode at the receivers. Such overhead can include increased use of the wireless medium for pilots supporting CSI estimation and increased feedback between receiving and transmitting entities on such CSI estimates.
As an example, assume that for each complex scalar value that defines the CSI between a single TX antenna and a single RX antenna (this type of CSI is often termed direct CSI by some in the Standards community) a fixed percentage Fcsi of wireless-channel resources is dedicated to pilots and/or feedback. One can easily see that as the dimension of the CSI required scales with quantities like NT, NR and/or K, the total CSI system-related overhead grows (e.g., by NT×Fcsi). For example, for K single antenna users, each with NT CSI scalar terms with respect to the transmitting antenna, there are KNT such scalars. Supporting an increase in the dimension of the CSI can take more wireless-channel resources, and reduces the amount of resources left for data transmission. This overhead increase can limit continued growth in throughput if spectral efficiency improvements do not offset increased CSI overheads.
The value Fcsi is often defined either by the system or by necessity given the coherence of channels in time and/or frequency. As the state of channels changes more rapidly in time and/or frequency, more resources may need to be used to estimate and keep track of CSI.
As an example, in a Frequency Division Duplex (FDD) based 3GPP Long Term Evolution (LTE) design, 8 symbols in a resource block of 12×14 OFDM symbols are used to support downlink pilots for each of the NT antennas. Simply considering system overheads for such pilots, and ignoring other CSI related overheads such as feedback, Fcsi can be as large as 8/168=4.76%. It means that with NT=8, assuming the pilot structure scales linearly with additional antennas, the total CSI-overhead could be as large as 38%, leaving 62% of symbols for supporting the remaining signaling overheads and data transmission. Clearly, such a system would not support unbounded increases in NT.
Thus, although symbols representing coded data information are used more efficiently, with increased robustness and/or spectral efficiency due to the increased DoFs by MIMO, the net spectral efficiency increases have to account for the CSI overhead. Thus, the net spectral efficiency growth is in fact less than that of individual data symbols as only a fraction of no more than (1−NT×Fcsi) of symbols can be used for data.
Recently a new class of techniques, termed “Blind Interference Alignment” (BIA) techniques, has demonstrated the ability to grow DoFs without requiring many of the CSI overheads of conventional MU-MIMO systems (see Wang et al., “Aiming Perfectly in the Dark—Blind Interference Alignment through Staggered Antenna Switching,” at http://arvix.org/abs/1002.2720). It is possible for a Multi-User MIMO (MU-MIMO) system with Nt transmission antennas at the BS and K single active-antenna users to achieve KNT/(K+NT−1) DoFs without CSIT. Thus, as K grows the system can approach the CSI-dependent upper bound of min(NT,K) DoFs. This is a striking result since it goes ahead of much of the conventional thinking and conjectures over recent decades, and it provides the potential to relieve the “dimensionality bottleneck” being faced by current systems.
For such a system to work, there is a requirement that the channels seen between the transmitting BS and the K users being served must be jointly changing in a predetermined way (with respect to the blind interference alignment scheme). This joint variation can be accomplished by employing many (physical) antenna elements and a single RF chain at each mobile terminal, where the single active-receive antenna of a user, i.e., the antenna driving the single RF chain of the user, can be varied over time. In other words, the single active receive antenna is a multi-mode antenna that is able to switch between, e.g., NT modes in a pre-determined fashion. The modes create independent (e.g., linearly independent) CSI vectors for the single user. Transmission also has to be confined to a suitable coherence interval in time over which the CSI in a given mode, though unknown to the system, is assumed to be effectively constant and different from mode to mode. The BIA technique works by creating a suitable antenna mode switching and combined data transmission vector over the K information bearing streams that are to be sent to the K users (one stream carries the intended information for one user).
Recently, a new class of MU-MIMO techniques has emerged, which take advantage of outdated CSIT to enable increases in DoFs via “Interference Alignment” (IA) at each of the receivers. What is attractive about these schemes is that the required CSIT is allowed to be fully outdated. In particular, these schemes enable DoF gains by only exploiting knowledge of past channels and rely on no knowledge of the current channel state at the transmitter (i.e., it requires no knowledge at the transmitter of the user channels over which transmission is about to take place). This is in sharp contrast to conventional MU-MIMO systems, whose efficacy intimately depends on the accuracy of the CSIT at the time of the data transmission. That is, the efficacy of conventional MU-MIMO intimately depends on how accurately the transmitter knows a priori the channels over which data transmission in MU-MIMO is to take place. It is possible for a Multi-User MIMO (MU-MIMO) system with Nt transmission antennas at the BS and L single-antenna users to achieve K/(1+1/2+1/3+ . . . +1/K) DoFs with outdated CSIT, where K=min(NT, L). As K grows the system DoFs grow as K/(γ+log(K)), where γ is the Euler-Mascheroni constant and is number between 0.57 and 0.58.
MU-MIMO schemes based on outdated CSI at the transmitter have some inherent challenges and limitations in the scenarios in which they are used. The first inherent issue is that they often require high Signal to Noise Ratios (SNR) to operate effectively. For example, the original IA scheme may require up to 20 dB of SNR. This is due to a property of the interference alignment process, which results in noise being amplified in the resulting interference-aligned streams. As a consequence of this, the original IA technique has limited application to many users in a cellular environment. For example, cell-edge users in conventional cellular often experience Signal-to-Interference-plus-Noise-Ratios (SINR) on the order of 0 dB or less, due to the interference coming from interfering cells not serving the K users. Many users, not just cell-edge users, do not have SINRs on the order of 20 dB or more. Because these schemes however can rely on completely outdated CSI, these schemes have less stringent requirements for user scheduling based on the collected CSIT.